import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
from tqdm import tqdm
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_image(image_file, input_size=448, max_num=12):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
path = '/mnt/lth/weights/Shanghai_AI_Laboratory/InternVL3-8B'
model = AutoModel.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    use_flash_attn=True,
    trust_remote_code=True).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)

# set the max number of tiles in `max_num`
generation_config = dict(max_new_tokens=1024, do_sample=True)


prompt = f"""
你是一个人体属性分类模型。请观察分析给定图片，并依据以下分类条目，从每个分类的选项中选出与图片相符的内容，按照字典格式输出结果，字典的键为分类名称，值为对应分类中符合图片情况的选项。分类条目如下：
- 性别：['女性', '男性']
- 年龄：['年龄儿童', '年龄成年']
- 发型：['长发', '短发', '光头']
- 配饰：['帽子', '墨镜', '口罩', '围巾']
- 上衣袖长：['短袖', '长袖']
- 上衣颜色：['上衣白色', '上衣黑色', '上衣蓝色', '上衣红色', '上衣绿色', '上衣黄色', '上衣紫色', '上衣粉色', '上衣灰色']
- 下装类型：['长裤', '短裤', '下装长裙', '下装短裙']
- 下装颜色：['下装白色', '下装黑色', '下装蓝色', '下装红色', '下装绿色', '下装黄色', '下装紫色', '下装粉色', '下装灰色']
- 携带物品：['手提包', '单肩包', '双肩包', '行李箱']
- 身高：['身长高', '身长中等', '身长矮']
- 体态：['体态胖', '体态中等', '体态瘦']

如果图片中没有对应的类别特征，那么对应的值就填写 '无明显特征'。

请严格按照示例的表达方式输出，不要有废话：
{{'性别': '女性', '年龄': '年龄成年', '发型': '长发', '上衣袖长':'短袖','配饰': '墨镜', '上衣颜色': '上衣白色', '下装类型': '长裤', '下装颜色': '下装黑色', '携带物品': '无明显特征', '身高': '身长中等', '体态': '体态中等'}}
"""

import pickle
import os
import pandas as pd
mem = {}
files = []
base_dir = '/mnt/lth'
with open('./intern_vl_8B_final_eval.pkl', 'wb') as f:
    # df = pd.read_csv('/mnt/lth/annotated_data.csv')
    # for row in df.iterrows():
    #     img_path = row[1].img_path
    #     files.append(os.path.join(base_dir,'/'.join(img_path.split('\\')[1:])))
    #     print(files[-1])
    base_dir = '/mnt/lth/final_eval/data'
    for file in tqdm(os.listdir(base_dir)):
        if file.endswith(('jpg','jpeg','bmp','png')):
            pixel_values = load_image(f'{os.path.join(base_dir,file)}', max_num=12).to(torch.bfloat16).cuda()
            response = model.chat(tokenizer, pixel_values, prompt, generation_config)
            mem[file] = response.replace('\n', '')
            print(response)
    pickle.dump(mem, f)

    # for file in tqdm(os.listdir('/mnt/lth/data_0409_x4')):
    #     if file.endswith('.jpg'):
    #         pixel_values = load_image(f'/mnt/lth/data_0409_x4/{file}', max_num=12).to(torch.bfloat16).cuda()
    #         response = model.chat(tokenizer, pixel_values, prompt, generation_config)
    #         mem[file] = response.replace('\n', '')
    #         print(response)
    # pickle.dump(mem, f)